{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tushare as ts\n",
    "ts.set_token('65e7ec2a529b0af0d5e759c7dc37c57acc95bde0b4e783b8b008c420')\n",
    "pro = ts.pro_api()\n",
    "df = pro.daily(ts_code='000001.SZ', start_date='20250101', end_date='20250820')\n",
    "df.to_csv('../data/raw/000001_daily.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Cell 1：环境 & 路径\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from pathlib import Path\n",
    "\n",
    "ROOT = Path('..')\n",
    "RAW   = ROOT/'data'/'raw'\n",
    "FONT = ROOT/'fonts'\n",
    "CHART = ROOT/'charts'\n",
    "CHART.mkdir(exist_ok=True)\n",
    "\n",
    "sns.set_style('whitegrid')\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']   # 中文"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cell 2：读净值 & 归一化\n",
    "rename_map = {\n",
    "    'date': 'date',\n",
    "    '成交日期': 'date',\n",
    "    '日期': 'date',\n",
    "    'trade_date': 'date'\n",
    "}\n",
    "# ===== 读上证指数数据 =====\n",
    "SHIndex = pd.read_csv(RAW/'000001_daily.csv', parse_dates=['trade_date'])\n",
    "SHIndex['nav'] = SHIndex['close'] / SHIndex['close'].iloc[0]   # 以首日=1 归一化\n",
    "SHIndex = SHIndex.rename(columns=rename_map)\n",
    "\n",
    "# ===== 读资产对账单（核心） =====\n",
    "# 列名：日期,成交金额,费用,市值,总资产,净出入金\n",
    "account  = pd.read_csv(RAW/'Tx_List_20250820.csv',\n",
    "                sep=None,               # 自动探测分隔符\n",
    "                engine='python',\n",
    "                skipinitialspace=True,\n",
    "                parse_dates=['日期'])\n",
    "account['日期'] = pd.to_datetime(account['日期'], errors='coerce')\n",
    "account  = account.sort_values('日期').reset_index(drop=True)\n",
    "\n",
    "account['pure_equity'] = account['总资产'] - account['净出入金'].fillna(0)  # 纯投资盈亏对应的日净资产\n",
    "account['day_factor'] = (account['pure_equity'] / account['pure_equity'].shift(1)).fillna(1)  # 计算日增长因子\n",
    "account['nav'] = account['day_factor'].cumprod()  # 生成净值序列（以日增长因子计算）\n",
    "\n",
    "account = account.rename(columns=rename_map)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cell 3：计算回撤 & 月度收益\n",
    "# eq = SHIndex.copy()\n",
    "eq = account.copy()\n",
    "eq['cummax'] = eq['nav'].cummax()\n",
    "eq['drawdown'] = (eq['nav'] - eq['cummax']) / eq['cummax']\n",
    "\n",
    "eq['month'] = eq['date'].dt.to_period('M')\n",
    "month_ret = eq.groupby('month')['nav'].apply(lambda x: x.iloc[-1]/x.iloc[0]-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "..\\fonts\\SimHei.ttf\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import font_manager\n",
    "# 正常显示中文\n",
    "font_path = FONT/'SimHei.ttf'  # 或 Step 1 得到的完整路径\n",
    "print(font_path)\n",
    "font_manager.fontManager.addfont(font_path)  # 重新加载\n",
    "my_font = font_manager.FontProperties(fname=font_path)\n",
    "plt.rcParams['font.family'] = my_font.get_name()\n",
    "plt.rcParams['axes.unicode_minus'] = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x250 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Cell 4：画三张图\n",
    "# 4-1 累计收益\n",
    "fig, ax = plt.subplots(figsize=(7,3))\n",
    "ax.plot(eq['date'], (eq['nav']-1)*100, color='#1f77b4')\n",
    "ax.set_title('累计收益 %'); ax.set_ylabel('收益 %')\n",
    "plt.tight_layout(); plt.savefig(CHART/'equity_curve.png', dpi=300)\n",
    "\n",
    "# 4-2 回撤\n",
    "fig, ax = plt.subplots(figsize=(7,2))\n",
    "ax.fill_between(eq['date'], eq['drawdown']*100, color='crimson', alpha=0.3)\n",
    "ax.set_title('回撤 %'); ax.set_ylabel('回撤 %')\n",
    "plt.tight_layout(); plt.savefig(CHART/'drawdown.png', dpi=300)\n",
    "\n",
    "# 4-3 月度收益\n",
    "fig, ax = plt.subplots(figsize=(6,2.5))\n",
    "month_ret.plot(kind='bar', ax=ax, color='steelblue')\n",
    "ax.set_title('月度收益 %'); ax.set_ylabel('收益 %')\n",
    "plt.xticks(rotation=0); plt.tight_layout()\n",
    "plt.savefig(CHART/'monthly_returns.png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>总收益</th>\n",
       "      <th>年化收益</th>\n",
       "      <th>Sharpe</th>\n",
       "      <th>最大回撤</th>\n",
       "      <th>日胜率</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-68.6%</td>\n",
       "      <td>-84.2%</td>\n",
       "      <td>-0.18</td>\n",
       "      <td>-72.0%</td>\n",
       "      <td>51.9%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      总收益    年化收益 Sharpe    最大回撤    日胜率\n",
       "0  -68.6%  -84.2%  -0.18  -72.0%  51.9%"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cell 5：统计表\n",
    "daily_ret = eq['nav'].pct_change().dropna()\n",
    "total_ret = eq['nav'].iloc[-1] - 1\n",
    "annual_ret = (1 + total_ret) ** (252/len(daily_ret)) - 1\n",
    "sharpe = daily_ret.mean() / daily_ret.std() * np.sqrt(252)\n",
    "max_dd = eq['drawdown'].min()\n",
    "win_rate = (daily_ret > 0).mean()\n",
    "\n",
    "stats = pd.DataFrame({\n",
    "    '总收益': [f'{total_ret:.1%}'],\n",
    "    '年化收益': [f'{annual_ret:.1%}'],\n",
    "    'Sharpe': [f'{sharpe:.2f}'],\n",
    "    '最大回撤': [f'{max_dd:.1%}'],\n",
    "    '日胜率': [f'{win_rate:.1%}']\n",
    "})\n",
    "stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0      1.000000\n",
      "1      0.988275\n",
      "2      0.978812\n",
      "3      0.991017\n",
      "4      0.998766\n",
      "         ...   \n",
      "154    1.387093\n",
      "155    1.412618\n",
      "156    1.438490\n",
      "157    1.498788\n",
      "158    0.637557\n",
      "Name: nav, Length: 159, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(eq['nav'])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "PyCharm (trade_env)",
   "language": "python",
   "name": "trade_env"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
